# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
		from PIL import Image




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		'''naver-clova-ix/donut-base-finetuned-docvqa'''
			__a    :					Optional[Any]     					=		(
			    '''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
			    '''should be the document containing the information, as well as a `question` that is the question about the '''
			    '''document. It returns a text that contains the answer to the question.'''
			)
			__a    :					Optional[int]     					=		'''document_qa'''
			__a    :					str     					=		AutoProcessor
			__a    :					Optional[Any]     					=		VisionEncoderDecoderModel

			__a    :					List[str]     					=		['''image''', '''text''']
			__a    :					List[str]     					=		['''text''']





			def __init__(  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   List[Any]:

							'''simple docstring'''


							if not is_vision_available():
											raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''		)

							super().__init__(*lowerCAmelCase__ ,  **lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
							__lowercase					     =				task_prompt.replace('''{user_input}''' ,  lowerCAmelCase__		)
							__lowercase					     =				self.pre_processor.tokenizer(
							    lowerCAmelCase__ ,  add_special_tokens=lowerCAmelCase__ ,  return_tensors='''pt'''		).input_ids
							__lowercase					     =				self.pre_processor(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values

							return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							return self.model.generate(
							    inputs['''pixel_values'''].to(self.device		) ,  decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device		) ,  max_length=self.model.decoder.config.max_position_embeddings ,  early_stopping=lowerCAmelCase__ ,  pad_token_id=self.pre_processor.tokenizer.pad_token_id ,  eos_token_id=self.pre_processor.tokenizer.eos_token_id ,  use_cache=lowerCAmelCase__ ,  num_beams=1 ,  bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] ,  return_dict_in_generate=lowerCAmelCase__ ,  ).sequences





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				self.pre_processor.batch_decode(lowerCAmelCase__		)[0]
							__lowercase					     =				sequence.replace(self.pre_processor.tokenizer.eos_token ,  ''''''		)
							__lowercase					     =				sequence.replace(self.pre_processor.tokenizer.pad_token ,  ''''''		)
							__lowercase					     =				re.sub(R'''<.*?>''' ,  '''''' ,  lowerCAmelCase__ ,  count=1		).strip()  # remove first task start token
							__lowercase					     =				self.pre_processor.tokenajson(lowerCAmelCase__		)

							return sequence["answer"]
def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				word.split()

				def justify(lowercase				,     lowercase				,     lowercase				) -> str:
								__lowercase					     =				max_width - width
								__lowercase					     =				len(lowercase				)
								if len(lowercase				) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowercase					     =				words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowercase					     =				spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowercase					     =				(
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowercase				):
																num_spaces_between_words_list[i] += 1
												__lowercase					     =				[]
												for i in range(lowercase				):
																# add the word
																aligned_words_list.append(line[i]				)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ''' '''				)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]				)
												# join the aligned words list to form a justified line
												return "".join(lowercase				)

				__lowercase					     =				[]
				__lowercase					     =				[]
				__lowercase					     =				0
				for word in words:
								if width + len(lowercase				) + len(lowercase				) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowercase				)
												width += len(lowercase				)
								else:
												# justify the line and add it to result
												answer.append(justify(lowercase				,     lowercase				,     lowercase				)				)
												# reset new line and new width
												__lowercase     ,	__lowercase					     =				[word], len(lowercase				)
				__lowercase					     =				max_width - width - len(lowercase				)
				answer.append(''' '''.join(lowercase				) + (remaining_spaces + 1) * ''' '''				)
				return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()